Modeling has become an important and efficient tool to predict microbial behavior in food safety and engineering. Foodborne pathogen infections, leading to foodborne illnesses and significant economic losses, are closely associated with public human health. Therefore, various mathematical models have been developed for food safety management, identifying critical control points, evaluating reformulations, and education in food engineering. These models can predict the microbial growth or response in different environments including temperature, moisture, pH, and concentration of preservatives in simulated or actual food systems. In this chapter, the most widely used traditional and novel food microbial predictive models are classified into primary, secondary, and tertiary models. They are introduced specifically for better understanding the theory, function, and systematical applications, as well as the origin, development process, state-of-the-art and the prospects of each model. In addition, the four specific growth models, i.e. Baranyi and Roberts model, square root model, response surface methodology, and artificial neural networks, as well as the software for Integrated Pathogen Modelling Program (IPMP 2013), are described in detail with an emphasis on the basic assumptions, limitations, and possible enhancements. The predictive models have commanded a bigger slice of the food safety engineering, such as in HACCP (Hazard Analysis Critical Control Point) and QMRA (Quantitative Microbiological Risk Assessment) programs with their increasing robustness and improving capabilities.